Accurate time correlation of data from different sensors is critical in the aircraft industry as a result of the high dynamics involved. For example, the navigation system of an aircraft traveling 1000 feet/second may have a 1 foot error as a result of a 1 millisecond time sync error in correlating inertial data to the Global Positioning System (GPS) measurements, although GPS measurements can be an order of magnitude more accurate. For many applications across a broad range of industries, accurate time correlation of data can be difficult and/or expensive to achieve with conventional approaches using custom hardware and real-time operating systems.
In the aircraft industry as well as other industries multiple sensors may be monitored by a single processor using the data to measure properties of a device, such as flexural properties of the wings of an aircraft. Multiple sensors may be employed to measure and determine differences between different portions or components of a system, such as the flexural and/or torsion state at different locations in an aircraft wing. In these examples, data measured at various time points are correlated for comparison with data collected from other sensors, and this is done in a common processor. These data acquisition systems apply time tags to data packets acquired by the sensors so the time tagged data may be interpolated to a common time frame for comparison.
There are numerous problems that may arise to adversely affect the correlation of time tagged data among sensors monitored by a single processor. For example, sensor clocks are known to drift with time. As the sensor clocks drift, there is a crossover period when one or more sensors generate data at nearly the same time. When one or more data packets arrive at the data acquisition processor while the processor has not completed time tagging the current data packet, each queued data packet will be tagged with a time bias error as it is processed, for which we use the term “crossover distortion.” This resulting time tag indicates an arrival time later than when data actually arrived. Additionally, as the sensor clocks drift, the arrival of packets in the time tagging queue can switch order, creating discontinuities in the time tag bias error for each sensor.